智能识别行人翻越护栏算法模型的图像训练数据

本数据集主要用于提升AI模型对行人违规翻越护栏行为的识别能力与精确性。通过对该数据集的训练，使AI模型能够精准识别行人攀爬跨越护栏等危险动作，并可应用于城市道路、高速公路、地铁站台及公园景区等重点区域的安防监控场景。同时，本数据集可为城市安全热点分析、护栏优化布设等智慧城市建设项目提供决策依据，提升公共安全管理智能化水平。
1.数据采集
通过企业自有摄像设备自行采集道路图像，同步记录图像ID、采集时间、设备型号、地理坐标、光照条件、天气状况等数据。
2.数据预处理与标注
通过数据清洗剔除模糊、重复图像。按7:2:1比例划分训练集/验证集/测试集。设置多级标注体系：
一级标签：合规通行/违规翻越
二级标签：攀爬护栏/跨越护栏/钻越护栏
辅助标注：行人边界框坐标、护栏区域边界框坐标。
3.模型选择与初始化
采用YOLOv8预训练模型，初始化参数并优化超参数：学习率0.001-0.0001动态调整，批量大小1-16动态调整，锚框参数适配行人姿态及护栏形态；集成姿态估计模块提升动作识别准确率。
4.模型训练
基于PyTorch实施分布式训练，采用混合精度训练（FP16）提升效率。设置训练时长，数据增强模拟复杂场景，添加动态模糊、遮挡物干扰等特效，模拟夜间低光照及雨雾天气条件。设置早停机制（patience=15），梯度裁剪：max_norm=1.0。
5.模型评估
在训练模型的过程中，使用验证集调整超参数，训练完成后在测试集上评估模型表现，评估指标包含：
基础性能指标：mAP@0.5、误报率
场景鲁棒性测试：雨雾天气检出率
并设置渐进式测试：单人翻越→多人同时翻越，标准护栏→异形护栏（如绿化带护栏）

This dataset is primarily designed to enhance the recognition capability and accuracy of AI models for identifying pedestrians' illegal fence-related behaviors. Training AI models on this dataset enables precise detection of dangerous actions such as pedestrians climbing, crossing, or crawling under guardrails, and can be applied to security monitoring scenarios in key areas including urban roads, expressways, subway platforms, and park scenic spots. Meanwhile, this dataset can provide decision-making support for smart city construction projects such as urban security hotspot analysis and optimized guardrail layout, thereby improving the intelligent level of public safety management.

1. Data Collection
Road images are collected using the enterprise's own camera equipment, while supporting data including image ID, collection time, device model, geographic coordinates, lighting conditions, and weather conditions are synchronously recorded.

2. Data Preprocessing and Annotation
Blurry and duplicate images are removed via data cleaning. The dataset is split into training/validation/test sets at a ratio of 7:2:1. A multi-level annotation system is established:
- Primary labels: Compliant Passage / Illegal Fence Violation
- Secondary labels: Climbing Guardrail, Crossing Guardrail, Crawling Under Guardrail
- Auxiliary annotations: Pedestrian bounding box coordinates, guardrail region bounding box coordinates.

3. Model Selection and Initialization
A pre-trained YOLOv8 model is adopted, with parameter initialization and hyperparameter optimization: dynamically adjust the learning rate between 0.001 and 0.0001, dynamically adjust the batch size between 1 and 16, and adapt anchor box parameters to pedestrian postures and guardrail shapes; a pose estimation module is integrated to improve the accuracy of action recognition.

4. Model Training
Distributed training is implemented based on PyTorch, and mixed-precision training (FP16) is adopted to improve efficiency. Training duration is set, and data augmentation is used to simulate complex scenarios: effects such as dynamic blur and occlusion interference are added to replicate low-light nighttime and rainy/foggy weather conditions. An early stopping mechanism (patience=15) and gradient clipping with max_norm=1.0 are configured.

5. Model Evaluation
During model training, the validation set is used to adjust hyperparameters, and after training is completed, the model performance is evaluated on the test set. The evaluation metrics include:
- Basic performance metrics: mAP@0.5, false positive rate
- Scenario robustness test: detection rate in rainy/foggy weather
Progressive testing is also set up: single-person crossing → multi-person simultaneous crossing, standard guardrails → special-shaped guardrails (e.g., green belt guardrails)

搜集汇总

背景与挑战

背景概述

该数据集是用于训练AI模型识别行人翻越护栏行为的图像数据，包含599条xlsx格式记录，每日更新；应用场景覆盖城市道路、高速公路等安防监控，通过YOLOv8模型和动态参数优化提升识别准确性和鲁棒性，支持智慧城市安全管理。